Most data association routines in implementation are based on a statistical measure that usually require that the track and measurement statistics are Gaussian. This Gaussian assumption has worked exceeding well for a number of years. Even when the Gaussian assumption was violated by the introduction of nonlinearities between the measurement and the track spaces, these techniques still performed well.
Recently, many data fusion algorithms have attemptd to incorporate environmental, terrain, and other sources of information. This new information can result in the severe loss of the Gaussian distribution. To overcome this problem, we have developed a fuzzy-logic based technique to perform association.
This association technique is based on a linguistic interpretation of the chi-squared metric. We use the inputs of the covariances and the residuals. This information is then processed using fuzzy memberships and inference engines and provides a probability score that a particular measurement associates with a given track. The two key elements of the routine are the 'normalization' of the residuals and the removals of covariance. First, we use the covariance information to define the parameters that describe the residual's membership functions. For example, if both covariances are large, the concept of small residual is much greater in absolute size than the case when both covariances are very small. Second, we incorporate the concept of the area of probability overlap between the two covariances. We can then remove portions of the area based on rules due to sensor blockage and incompatible terrain.